title: A study of morphology-based wavelet features and multiple-wavelet strategy for EEG signal classification: results and selected statistical analysis.
abstract: Automatic detection and classification of Epileptiform transients is an open and important clinical issue. In this paper, we test 5 feature sets derived from a group of morphology-based wavelet features and compare the results with that of a Guler-suggested feature set. We also implement a multiple-mother-wavelet strategy and compare performance with the usual single-mother-wavelet strategy. The results indicate that both the derived features and the multiple-mother-wavelet strategy improved classifier performance, using a variety of performance measures. We assess the statistical significance of the performance improvement of the new feature sets/strategy. In most cases, the performance improvement is either significant or highly significant.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhou
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Schalkoff
authors.forename: Brian C
authors.initials: BC
authors.lastname: Dean
authors.forename: Jonathan J
authors.initials: JJ
authors.lastname: Halford
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004569:Electroencephalography; D006801:Humans; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software; D058067:Wavelet Analysis